• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.995-1007
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• 2019

The purpose of this study was to investigate the effects of 12-week elastic band exercise on body composition, blood lipids and AMPK in 24 elderly female volunteers aged 65-75 years, and they were divided into the combined exercise group(n=12) and the control group(n=12). The elastic band exercise method was to do exercise 3 times a week for 60 minutes per session, 1-4 weeks for low intensity of OMNI-RES 3-4, 5-8 weeks for medium intensity of OMNI-RES 5-6, 9-12 weeks for OMNI-RES 7-8 of high intensity. In order to compare the differences in the groups before and after the elastic band exercise, two-way repeated measures ANOVA was used to verify the interaction between group and time. The difference in the groups of the measured data was paired t-test, the difference between the groups was paired independent t-test, and analysis of covariance ANCOVA was performed to minimize the inter-group error. The statistical significance level of each item was set to .05. As a result, body fat percentage of exercise group significantly decreased (p<.05), and skeletal muscle volume was significantly increased (p<.01). TC, TG and LDL-C were not significantly different between the exercise and control groups, and HDL-C was significantly decreased in the control group (p<.05). AMPK was significantly decreased in the exercise group (p<.001), but there was no significant difference in the control group. According to the covariance analysis to minimize the error of difference between the pre-exercise groups (p<.05), there was significant difference in AMPK of groups after exercise. These results suggest that the 12-week elastic band exercise has a positive effect on the body composition and AMPK of the elderly women.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.409-418
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• 2021

Conformal coating is a technology that protects PCB(Printed Circuit Board) and minimizes PCB failures. Since the defects in the coating are linked to failure of the PCB, the coating surface is examined for air bubbles to satisfy the successful conditions of the conformal coating. In this paper, we propose an algorithm for detecting problematic bubbles in high-risk groups by applying image signal processing. The algorithm consists of finding candidates for problematic bubbles and verifying candidates. Bubbles do not appear in visible light images, but can be visually distinguished from UV(Ultra Violet) light sources. In particular the center of the problematic bubble is dark in brightness and the border is high in brightness. In the paper, these brightness characteristics are called valley and mountain features, and the areas where both characteristics appear at the same time are candidates for problematic bubbles. However, it is necessary to verify candidates because there may be candidates who are not bubbles. In the candidate verification phase, we used convolutional neural network models, and ResNet performed best compared to other models. The algorithms presented in this paper showed the performance of precision 0.805, recall 0.763, and f1-score 0.767, and these results show sufficient potential for bubble test automation.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.2
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• pp.121-125
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• 2010

Carbon Capture and Storage (CCS) technology mitigates the emission amount of carbon dioxide into the atmosphere and can reduce green house effect which causes the climate change. Deep saline aquifer or obsolete oil/gas storage etc. in the marine geological structure are considered as the candidates for the storage. The injection and storage relating technology have been interested in the global society, however the adverse effect caused by leakage from the system failure. Even the safety level of the CCS is very high and there is almost no possibility to leak but, still the risk to marine ecosystem of the high concentrated carbon dioxide exposure is not verified. The present study introduces the system and environmental risk assessment methods. The feature, event and process approach can be a good starting point and we found the some possibility from the fault tree analysis for evaluation. From the FEP analysis, we drove the possible scenario which we need to concentrate on the construction and operation stages.

• Journal of Korean Society on Water Environment
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• v.22 no.2
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• pp.294-299
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• 2006

The Ministry of Environment in Korea has introduced Total Pollution Load Management System (TPLMS) in major 4 large rivers to protect the water quality from possible pollutants. In order to successfully achieve the TPLMS, the nonpoint source should be controled by applying the best management practices in highly polluted areas. Of the various nonpoint sources, the highways are stormwater intensive landuses because of its high imperviousness and high pollutant mass emissions. The EMC (Event Mean Concentration) is an important parameter to correctly determine the pollutant mass loadings from nonpoint sources. However, it has wide ranges because of various reasons such as first flush phenomenon, rainfall and watershed characteristics. Even though the EMC is closely related to the first flush phenomenon, the relationship have not proven until present. Therefore, in this paper, the dynamic EMC method will be introduced to clearly make the relationship between EMC and first flush phenomenon. Also by applying the dynamic EMC method to monitored data, we found that the highly concentrated stormwater runoff was washed off within 20~50 minutes storm duration. The first flush criteria for economical treatment was also determined to 5~10 mm (mean=7.4 mm) as a cumulative rainfall.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.463-468
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• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.8
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• pp.723-730
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• 2003

The CN(carbon nitrogen) nanofibers were formed by HIP(high isostatic pressure) process. From the field emission measurement, CN nanofibers shows an excellent characteristics of emitter, better than CNTs and carbon nanofibers. The structures obtained can be divided into three groups : bamboo-like fibers, corrugated structures and bead necklace-like fib res. Emission properties of CN nanofibers were investigated for spacing, between anode and cathode, variation. Turn-on fields was 1.4 v/$\mu\textrm{m}$. The time reliability and light emission test were carried out for about 100 hours. We suggest that CN nanofibers can be possibly applied to the high brightness flat lamp because of low turn-on field and time reliability

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.273-280
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• 2005

Robust magneto-telluric (MT) response function estimators are now in standard use in electromagnetic induction research. Properly devised and applied, these methods can reduce the influence of unusual data (outlier) in the response (electric field) variable, but often not sensitive to exceptional predictor (magnetic field) data, which are termed leverage points. A bounded influence estimator is described which simultaneously limits the influence of both outlier and leverage point, and has proven to consistently yield more reliable MT response function estimates than conventional robust approach. The bounded influence estimator combines a standard robust M-estimator with leverage weighting based on the statistics of the hat matrix diagonal, which is a standard statistical measure of unusual predictors. Further extensions to MT data analysis are proposed, including a establishment of data rejection criterion which minimize the influence of both electric and magnetic outlier in frequency domain based on statistical distribution of electromagnetic field. The rejection scheme made in this study seems to have an effective performance on eliminating extreme data, which is even not removed by BI estimator, in frequency domain. The effectiveness and advantage of these developments are illustrated using real MT data.

• Economic and Environmental Geology
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• v.40 no.1 s.182
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• pp.115-128
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• 2007

This study proposed a modified logistic regression model for a probabilistic prediction of debris flow on natural terrain at the granitic rock area. The modified model dose not contain any categorical factors that were used in the previous model and secured higher reliability of prediction than that of the previous one. The modified model is composed of lithology, two factors of geomorphology, and three factors of soil property. Verification result shows that the prediction reliability is more than 86%. Using the modified regression model, the landslide prediction maps were established. In case of Sacheon area, the prediction map showed that the landslide occurrence was not well corresponded with the model since, even though the forest-fred area was distributed on the center of the model, no factors were considered for the landslide predictions. On the other hand, the prediction model was well corresponded with landslide occurrence at Jumunjin-Yeongok area. The prediction model developed in this study has very high availability to employ in other granitic areas.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Journal of the Korean Geotechnical Society
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• v.20 no.9
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• pp.25-32
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• 2004

Some amount of cements can be added into the soil with high water content to improve the engineering properties. In such a case, it is difficult to predict and figure out the phase changes of the soil-cement mixture which is closely associated with workability of the soil-cement mixture. Changes in heat of hydration and hardness of the cement pastes are known to provide the useful information about the phase changes of the soil-cement mixtures. In this study, heat of hydration and cone penetration depth were measured from the specimens of cement paste and 3 soil-cement mixtures. From the experimental results, it was found that the phase changes of the soil-cement mixtures are the same as those of cement paste, and that shear strength of the mixtures abruptly increases when the heat of hydration is minimum. Initial setting time of the mixtures coincides with the state when fall cone penetration depth was 1.0 mm and it is defined as plastic limit of the mixtures. Initial setting time of the mixtures is retarded as soil/cement ratio is increased. Measurements of heat of hydration and fall cone apparatus could be the useful tools to predict the phase changes of tile soil-cement mixtures.